Three dimensional localization of a moving target for adaptive radiation therapy

ABSTRACT

The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate a target in a three-dimensional (3D) volume. For example, an image acquisition device may provide a 3D medical image containing a region of interest of the target. A processor may then extract a plurality of two-dimensional (2D) slices from the 3D image. The processor may also determine a 2D patch for each 2D slice, wherein the 2D patch corresponds to an area of the 2D slice associated with the target. The processor may also convert the 2D patch to an adaptive filter model for determining a location of the region of interest.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is related to U.S. Ser. No. 14/607,654 filedJan. 28, 2015 and titled “Three Dimensional Localization and Trackingfor Adaptive Radiation Therapy,” the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The disclosure generally relates to radiation therapy or radiotherapy.More specifically, this disclosure relates to systems and methods forthree-dimensional tracking of a tumor for use in developing a radiationtherapy treatment plan to be used during radiotherapy.

BACKGROUND

Radiotherapy is used to treat cancers and other ailments in mammalian(e.g., human and animal) tissue. One such radiotherapy technique is aGamma Knife, by which a patient is irradiated by a large number oflow-intensity gamma rays that converge with high intensity and highprecision at a target (e.g., a tumor). In another embodiment,radiotherapy is provided using a linear accelerator, whereby a tumor isirradiated by high-energy particles (e.g., electrons, protons, ions, andthe like). The placement and dose of the radiation beam must beaccurately controlled to ensure the tumor receives the prescribedradiation, and the placement of the beam should be such as to minimizedamage to the surrounding healthy tissue, often called the organ(s) atrisk (OARs).

The radiation beam may be shaped to match a shape of the tumor, such asby using a multileaf collimator (e.g., multileaf collimator includesmultiple tungsten leaves that may move independently of one another tocreate customized radiation beam shapes). (Radiation is termed“prescribed” because a physician orders a predefined amount of radiationto the tumor and surrounding organs similar to a prescription formedicine).

Traditionally, for each patient, a radiation therapy treatment plan(“treatment plan”) may be created using an optimization technique basedon clinical and dosimetric objectives and constraints (e.g., themaximum, minimum, and mean doses of radiation to the tumor and criticalorgans). The treatment planning procedure may include using athree-dimensional image of the patient to identify a target region(e.g., the tumor) and to identify critical organs near the tumor.Creation of a treatment plan can be a time consuming process where aplanner tries to comply with various treatment objectives or constraints(e.g., dose volume histogram (DVH) objectives), taking into accounttheir individual importance (e.g., weighting) in order to produce atreatment plan which is clinically acceptable. This task can be atime-consuming trial-and-error process that is complicated by thevarious organs at risk (OARs, because as the number of OARs increases(e.g., up to thirteen for a head-and-neck treatment), so does thecomplexity of the process. OARs distant from a tumor may be easilyspared from radiation, while OARs close to or overlapping a target tumormay be difficult to spare.

Computed Tomography (CT) imaging traditionally serves as the primarysource of image data for treatment planning for radiation therapy. CTimages offer accurate representation of patient geometry, and CT valuescan be directly converted to electron densities (e.g., Hounsfield units)for radiation dose calculation. However, using CT causes the patient tobe exposed to additional radiation dosage. In addition to CT images,magnetic resonance imaging (MRI) scans can be used in radiation therapydue to their superior soft-tissue contrast, as compared to CT images.MRI is free of ionizing radiation and can be used to capture functionalinformation of the human body, such as tissue metabolism andfunctionality.

Imaging systems such as computed tomography (CT), fluoroscopy, andmagnetic resonance imaging (MRI) may be used to determine the locationof and track a target (e.g., an organ, a tumor, and the like). MRI istypically used because it provides excellent soft tissue contractwithout using ionizing radiation as used by CT. An example of aradiotherapy treatment system integrated with an imaging system mayinclude an MRI-Linac, which may use three-dimensional (3D) images of atarget (e.g., a tumor). The MRI apparatus of the MRI-Linac may provide aplurality of images that corresponds to a partial map of hydrogen nucleiin tissues of the patient. The patient images may be acquired in atwo-dimensional (2D) plane or in a 3D volume. Because organs and tumorsmove within a patient's body, fast and accurate 3D localization of thetarget is important. For instance, a target organ or tumor may movebecause of various types of motion (e.g., respiratory, cardiac,peristalsis or other types of patient motion). However, 2D MR slices aretypically acquired at a particular location of the patient's body andthe 2D MR slice may not include the tumor/target because of the targetorgan or tumor motion. Therefore, a system and method to acquire thetarget tumor and track the structure and motion of the tumor is requiredsuch that the acquired 2D MR slice in terms of location, orientation,and/or thickness includes the target tumor and is visible on the 2D MRslices in a clinical environment.

This Overview is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the invention will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof, each of which are not tobe taken in a limiting sense. The scope of the present invention isdefined by the appended claims and their legal equivalents.

SUMMARY

Certain embodiments of the present disclosure relate to a method formedical image processing. The method may be implemented by a processordevice executing a plurality of computer executable instructions. Themethod may be implemented by a processor, for locating a target in athree-dimensional (3D) volume, and comprise: receiving, from an imageacquisition device, a 3D medical image containing a region of interestof the target; extracting, by the processor, a plurality oftwo-dimensional (2D) slices from the 3D image; determining, by theprocessor, a 2D patch for each 2D slice, wherein the 2D patchcorresponds to an area of the 2D slice associated with the target; andconverting, by the processor, the 2D patch to an adaptive filter modelfor determining a location of the region of interest.

Certain embodiments of the present disclosure relate to a medical imageprocessing system. The system may track a three-dimensional (3D) targetin a volume using two-dimensional imaging slices of the volume, andcomprise: a processor; and a memory operatively coupled to the processorand storing computer-executable instructions that when executed by theprocessor, causes the processor to perform the method, comprising:receiving, from an image acquisition device, a 3D medical image of the3D volume containing a region of interest of the target, wherein the 3Dimage is stored in the memory; extracting, by the processor, a pluralityof two-dimensional (2D) slices from the 3D image; determining, by theprocessor, a 2D patch for each 2D slice, wherein the 2D patchcorresponds to an area of the 2D slice associated with the target; andconverting, by the processor, the 2D patch to an adaptive filter modelfor determining a location of the region of interest.

Additional objects and advantages of the present disclosure will be setforth in part in the following detailed description, and in part will beobvious from the description, or may be learned by practice of thepresent disclosure. The objects and advantages of the present disclosurewill be realized and attained by means of the elements and combinationsparticularly pointed out in the appended claims.

It is to be understood that the foregoing general description and thefollowing detailed description are exemplary and explanatory only, andare not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingletter suffixes or different letter suffixes may represent differentinstances of similar components. The drawings illustrate generally, byway of example, but not by way of limitation, various embodiments, andtogether with the description and claims, serve to explain the disclosedembodiments. When appropriate, the same reference numbers are usedthroughout the drawings to refer to the same or like parts. Suchembodiments are demonstrative and not intended to be exhaustive orexclusive embodiments of the present apparatuses, systems, or methods.

FIG. 1 illustrates an exemplary radiotherapy system to localize andtrack a three-dimensional target for radiation therapy.

FIG. 2 illustrates a radiotherapy device, a Gamma Knife, used in theradiotherapy system of FIG. 1.

FIG. 3 illustrates a radiotherapy device, a linear accelerator, used inthe radiotherapy system of FIG. 1.

FIG. 4 illustrates an exemplary flowchart for a training module togenerate an adaptive filter model.

FIG. 5 illustrates an exemplary flowchart for using one or more trainedadaptive filter models to localize and track a tumor during radiationtreatment of a patient.

FIG. 6 is a pictorial illustration of an adaptive filter model, aresponse map, and an image of tracking a target in a patient.

DETAILED DESCRIPTION

In this disclosure, a radiotherapy system and a corresponding method forlocalizing and tracking a tumor in the anatomy of a patient undergoingradiation therapy treatment are introduced. In particular, duringradiation therapy treatment planning a plurality 3D magnetic resonanceimages (MRI) or 4D MRI images are captured. The exact location of atarget, for example, a tumor in the anatomy of the patient can bedetermined by the radiotherapy system using segmentation techniques,known in the art. After, the location of the tumor is determined, thesystem can generate a plurality of patches and can determine an offsetfrom a center of the patch to the tumor. A variety of methods, such ascorrelation or using appearance model techniques, can be used by theradiotherapy system to enhance selected features of the plurality ofpatches, which result in a plurality of adaptive filter models. Theseadaptive filter models can then be used by the radiotherapy systemduring actual “on-line” treatment of a patient.

One advantage of this approach is a target tumor can be localized andtracked during radiotherapy treatment. For a tumor typically moveswithin the anatomy of the patient because of a variety of types ofmovement, such as motion caused by respiration, cardiac motion,peristalsis, involuntary motion of the patient, (e.g., a cough, asneeze, etc.), or voluntary motion caused by the patient being on atreatment table during radiotherapy. When utilizing MRI-guided radiationtherapy, only 2D slices of images of the patient's anatomy areavailable. This approach permits determining which 2D slice(s) includethe tumor and further advantageously provides an ability to track thetumor by estimating a potential future location.

FIG. 1 illustrates an exemplary radiotherapy system 100 for performingtarget localization and tracking during radiation therapy treatment.Radiotherapy system 100 may include a radiation therapy device 110connected to a network 120 that is connected to an internet 130. Thenetwork 120 can connect the radiation therapy device 110 with a database140, a hospital database 142, an oncology information system (OIS) 150(e.g., which may provide patient information), a treatment planningsystem (TPS) 160 (e.g., for generating radiation therapy treatment plansto be used by the radiotherapy device 110), an image acquisition device170, a display device 180 and an user interface 190.

The radiotherapy device 110 may include a processor 112, a memory device116, and a communication interface 114. Memory device 116 may storecomputer executable instructions, for an operating system 118, treatmentplanning software 120, a training module 124 that generates an adaptivefilter 126, and a target localization module 120 and any other computerexecutable instructions to be executed by the processor 240.

Processor 112 may be communicatively coupled to the memory device 116,and processor 112 may be configured to execute computer executableinstructions stored thereon. For example, processor 112 may executetraining module 124 to implement functionalities of both the trainingmodule 124 and functionalities of the target localization module 128 inorder to determine a location of the target in a patient duringadministration of radiotherapy. In addition, processor 112 may executethe treatment planning software 120 (e.g., such as Monaco® softwaremanufactured by Elekta) that may interface with training module 124 andtarget localization module 128.

The processor 112 may be a processing device, include one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit (CPU), graphics processing unit (GPU), an acceleratedprocessing unit (APU), or the like. More particularly, processor 112 maybe a complex instruction set computing (CISC) microprocessor, reducedinstruction set computing (RISC) microprocessor, very long instructionWord (VLIW) microprocessor, a processor implementing other instructionsets, or processors implementing a combination of instruction sets.Processor 112 may also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP), aSystem on a Chip (SoC), or the like. As would be appreciated by thoseskilled in the art, in some embodiments, processor 112 may be aspecial-purpose processor, rather than a general-purpose processor.Processor 112 may include one or more known processing devices, such asa microprocessor from the Pentium™, Core™, Xeon™, or Itanium® familymanufactured by Intel™, the Turion™, Athlon™, Sempron™, Opteron™, FX™,Phenom™ family manufactured by AMD™, or any of various processorsmanufactured by Sun Microsystems. Processor 112 may also includegraphical processing units such as a GPU from the GeForce®, Quadro®,Tesla® family manufactured by Nvidia™, GMA, Iris™ family manufactured byIntel™, or the Radeon™ family manufactured by AMD™. Processor 112 mayalso include accelerated processing units such as the Desktop A-4(6,8)Series manufactured by AMD™, the Xeon Phi™ family manufactured byIntel™. The disclosed embodiments are not limited to any type ofprocessor(s) otherwise configured to meet the computing demands ofidentifying, analyzing, maintaining, generating, and/or providing largeamounts of imaging data or manipulating such imaging data to localizeand track a target or to manipulate any other type of data consistentwith the disclosed embodiments. In addition, the term “processor” mayinclude more than one processor, for example, a multi-core design or aplurality of processors each having a multi-core design. Processor 112can execute sequences of computer program instructions, stored in memory116, to perform various operations, processes, methods that will beexplained in greater detail below.

Memory device 116 can store image data 122 (e.g., 3D MRI, 4D MRI, 2Dslices, etc.) received from the image acquisition device 179, or anyother type of data/information in any format that the radiotherapydevice 110 may use to perform operations consistent with the disclosedembodiments. Memory device 210 may include a read-only memory (ROM), aflash memory, a random access memory (RAM), a dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM, a staticmemory (e.g., flash memory, static random access memory), etc., on whichcomputer executable instructions are stored in any format. The computerprogram instructions can be accessed by the processor 112, read from theROM, or any other suitable memory location, and loaded into the RAM forexecution by the processor 112. For example, memory 116 may store one ormore software applications. Software applications stored in memory 116may include, for example, an operating system 118 for common computersystems as well as for software-controlled devices. Further, memory 116may store an entire software application or only a part of a softwareapplication that is executable by processor 112. For example, memorydevice 116 may store one or more radiation therapy treatment plans astreatment planning software 120 generated by the treatment planningsystem 160.

In some embodiments, memory device 116 may include a machine-readablestorage medium. While the machine-readable storage medium in anembodiment may be a single medium, the term “machine-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of computer executableinstructions or data. The term “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “machine readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical and magnetic media. For example, the memory/storagedevice 210 can be one or more volatile, non-transitory, or non-volatiletangible computer-readable media.

The radiotherapy device 110 can communicate with a network 130 via acommunication interface 114, which is communicatively coupled toprocessor 112 and memory 116. Communication interface 114 may include,for example, a network adaptor, a cable connector, a serial connector, aUSB connector, a parallel connector, a high-speed data transmissionadaptor (e.g., such as fiber, USB 3.0, thunderbolt, and the like), awireless network adaptor (e.g., such as a WiFi adaptor), atelecommunication adaptor (e.g., 3G, 4G/LTE and the like), and the like.Communication interface 114 may include one or more digital and/oranalog communication devices that permit radiotherapy device 110 tocommunicate with other machines and devices, such as remotely locatedcomponents, via a network 130.

The network 130 may provide the functionality of a local area network(LAN), a wireless network, a cloud computing environment (e.g., softwareas a service, platform as a service, infrastructure as a service, etc.),a client-server, a wide area network (WAN), and the like. Therefore,network 130 can allow data transmission between the radiotherapy device110 and a number of various other systems and devices, such as forexample: the treatment planning system 160, the Oncology InformationSystem 150, and the image acquisition device 170. Further, datagenerated by the treatment planning system 160, the OIS 150, and theimage acquisition device 170 may be stored in the memory 116, database140, or hospital databased 142. The data may be transmitted/received vianetwork 130, through communication interface 114 in order to be accessedby the processor 112, as required.

In addition, the network 130 may be connected to the internet 132 tocommunicate with servers or clients that reside remotely and areconnected to the internet. As described, network 130 may include othersystems S1 (134), S2 (136), S3 (138). Systems S1, S2, and/or S3 may beidentical to system 100 or may be different systems. In someembodiments, one or more systems connected to network 130 may form adistributed computing/simulation environment that collaborativelyperforms image acquisition, target location and target tracking as wellother aspects of providing radiotherapy to a patient.

Additionally, radiotherapy system 100 may communicate with the database140 or the hospital database 142 in order to execute one or moreprograms stored remotely. By way of example, database 140, hospitaldatabase 142, or both may include relational databases such as Oracle™databases, Sybase™ databases, or others and may include non-relationaldatabases, such as Hadoop sequence files, HBase, Cassandra, or others.Such remote programs may include, for example, oncology informationsystem (OIS) software or treatment planning software. The OIS software,for instance, may be stored on the hospital database 142, the database140, or the OIS 150. The treatment planning software, for example, maybe stored on the database 140, the hospital database 142, the treatmentplanning system 160 or the OIS 150. Thus, for instance, radiotherapydevice 110 may communicate with the hospital database 142 to implementfunctionalities of the oncology information system 150.

Systems and methods of disclosed embodiments, however, are not limitedto separate databases. In one aspect, radiotherapy system 100 mayinclude database 220 or hospital database 230. Alternatively, database220 and/or hospital database 230 may be located remotely from theradiotherapy system 100. Database 140 and hospital database 142 mayinclude computing components (e.g., database management system, databaseserver, etc.) configured to receive and process requests for data storedin memory devices of database 140 or hospital database 142 and toprovide data from database 220 or hospital database(s) 230. One skilledin the art would appreciate that databases 140, 142 may include aplurality of devices located either in a central or distributed manner.

In addition, radiotherapy device 110 may communicate with database 140through network 130 to send/receive a plurality of various types of datastored on database 140. For example, in some embodiments, database 140may be configured to store a plurality of images (e.g., 3D MRI, 4DMRI,2D MRI slice images, CT images, 2D Fluoroscopy images, X-ray images, rawdata from MR scans or CT scans, Digital Imaging and Communications inMedicine (DIMCOM) data, etc.) from image acquisition device 140.Database 140 may store data to be used by the target localization module128, the training module 124, and the treatment planning software 120.The radiation therapy device 110 may receive the imaging data (e.g.,3DMRI images, 4D MRI images) from the database 120 to order to generatea plurality of adaptive filters models, as described below.

Further, the radiotherapy system 100 can include an image acquisitiondevice 170 that can acquire medical images (e.g., Magnetic ResonanceImaging (MRI) images, 3D MRI, 2D streaming MRI, 4D volumetric MRI,Computed Tomography (CT) images, Cone-Beam CT, Positron EmissionTomography (PET) images, functional MRI images (e.g., fMRI, DCE-MRI anddiffusion MRI), X-ray images, fluoroscopic image, ultrasound images,radiotherapy portal images, single-photo emission computed tomography(SPECT) images, and the like) of the patient. Image acquisition device170 may, for example, be an MRI imaging device, a CT imaging device, aPET imaging device, an ultrasound device, a fluoroscopic device, a SPECTimaging device, or other medical imaging device for obtaining one ormore medical images of the patient. Images acquired by the imagingacquisition device 170 can be stored within database 140 as eitherimaging data and/or test data. By way of example, the images acquired bythe imaging acquisition device 170 can be also stored by theradiotherapy device 110 in memory 116.

In an embodiment, for example, the image acquisition device 140 may beintegrated with the radiotherapy device 110 as a single apparatus (e.g.,a MRI device combined with a linear accelerator, also referred to as a“MRI-Linac” or as an integrated MRI device combined with a Gamma Knife).Such a MRI-Linac can be used, for example, to determine a location of atarget organ or a target tumor in the patient, such as to directradiation therapy according to the radiation therapy treatment plan to apredetermined target.

The image acquisition device 170 can be configured to acquire one ormore images of the patient's anatomy for a region of interest (e.g., atarget organ, a target tumor or both). Each 2D slice can include one ormore parameters (e.g., a 2D slice thickness, an orientation, and alocation, etc.). The one or more parameters can be adjusted by using theprocessor 112, to include the target. For instance, selected features ofthe 2D slice can be manipulated, e.g., by adjusting the gradient or RFwaveform properties. For example, the position of the slice can bevaried by changing the basic frequency of the RF pulse and maintainingthe same gradient strength. Further, the orientation of the slice can bevaried, for example, by using a physically different gradient axis(e.g., the selected slice can be orthogonal to the gradient applied). Inan example, the image acquisition device 170 (e.g., an MRI or anMRI-Linac) can acquire a 2D slice in any orientation. For example, anorientation of the 2D slice can include a sagittal orientation, acoronal orientation, or an axial orientation. These orientations cancorrespond to a magnetic field gradient (e.g., Gx, Gy, or Gz,respectively) associated with the MRI or the MRI-Linac. The processor112 can adjust a parameter, such as the thickness of the 2D slice, toinclude the target organ or target tumor. In an example, the thicknessof the slice can be calculated and adjusted such as by using Equation 1:

$\begin{matrix}{T = \frac{2\Delta\;\omega_{s}}{\gamma\; G_{slice}}} & \left( {{Eq}.\mspace{11mu} 1} \right)\end{matrix}$

In Equation 1, T can represent the 2D slice thickness, such as can bemeasured in units of distance (e.g., millimeters). Δω_(s) can representan excitation bandwidth corresponding to a radio frequency pulse appliedat a specified frequency (e.g., Hertz, “Hz”). The letter, γ, canrepresent a constant called the gyromagnetic ratio (e.g., for protons, γhas a value of 267.54 MHz/Tesla). G_(slice) can represent the magneticfield gradient (e.g., measured in Tesla/meters). In an example, 2Dslices can be determined from information such as a 3D MRI volume. Such2D slices can be acquired by the image acquisition device 170 in“real-time” while a patient is undergoing radiation therapy treatment,for example, when using the radiation therapy device 110.

The treatment planning system 160 may generate and store radiationtherapy treatment plans for a particular patient to be treated,radiation therapy treatment plans for other patients, as well as otherradiotherapy information (e.g., beam angles, dose-histogram-volumeinformation, the number of radiation beams to be used during therapy,the beam angles, the dose per beam, and the like). For example,treatment planning system 160 may provide information about a particularradiation dose to be applied to the patient and other radiotherapyrelated information (e.g., type of therapy: such as image guidedradiation therapy (IGRT), intensity modulated radiation therapy (IMRT),stereotactic radiotherapy; and the like).

Generating the treatment plan may include communicating with the imageacquisition device 170 (e.g., a CT device, a MRI device, a PET device,an X-ray device, an ultrasound device, etc.) in order to access imagesof the patient and to delineate a target, such as a tumor. In someembodiments, the delineation of one or more organs at risk (OARs), suchas healthy tissue surrounding the tumor or in close proximity to thetumor may be required. Therefore, segmentation of the OAR may beperformed when the OAR is close to the target tumor. In addition, if thetarget tumor is close to the OAR (e.g., prostate in near proximity tothe bladder and rectum), segmentation of the OAR, the treatment planningsystem 160 may allow study of the dose distribution not only in thetarget, but also in the OAR.

In order to delineate a target organ or a target tumor from the OAR,medical images, such as MRI images, CT images, PET images, fMRI images,X-ray images, ultrasound images, radiotherapy portal images, SPECTimages and the like, of the patient undergoing radiotherapy may beobtained non-invasively by the image acquisition device 170 to revealthe internal structure of a body part. Based on the information from themedical images, a 3D structure may be obtained. In addition, during atreatment planning process, many parameters may be taken intoconsideration to achieve a balance between efficient treatment of thetarget tumor (e.g., such that the target tumor receives enough radiationdose for an effective therapy) and low irradiation of the OAR(s) (e.g.,the OAR(s) receives as low a radiation dose as possible), the locationof the target organ and the target tumor, the location of the OAR, andthe movement of the target in relation to the OAR. For example, the 3Dstructure may be obtained by contouring the target or contouring the OARwithin each 2D layer or slice of an MRI or CT image and combining thecontour of each 2D layer or slice. The contour may be generated manually(e.g., by a physician, dosimetrist, or health care worker) orautomatically (e.g., using a program such as the Atlas-basedAuto-segmentation software, ABAS®, manufactured by Elekta, AB,Stockholm, Sweden). In certain embodiments, the 3D structure of a targettumor or an OAR may be generated automatically by the treatment planningsystem 160.

After the target tumor and the OAR(s) have been located and delineated,a dosimetrist, physician or healthcare worker may determine a dose ofradiation to be applied to the target tumor and any OAR proximate to thetumor (e.g., left and right parotid, optic nerves, eyes, lens, innerears, spinal cord, brain stem, and the like). After the radiation doseis determined for each anatomical structure (e.g., target tumor, OAR), aprocess known as inverse planning may be performed to determine one ormore treatment plan parameters, such as volume delineation (e.g., definetarget volumes, contour sensitive structures), margins around the targettumor and OARs, dose constraints (e.g., full dose to the tumor targetand zero dose to any OAR; 95% of dose to PTV, while spinal cord ≦45 Gy,brain stem ≦55 Gy, and optic structures <54 Gy receive said dosagesrespectively), beam angle selection, collimator settings, and beam-ontimes. The result of inverse planning may constitute a radiation therapytreatment plan that may be stored in the treatment planning system 160or database 140. Some of these treatment parameters may be correlated.For example, tuning one parameter (e.g., weights for differentobjectives, such as increasing the dose to the target tumor) in anattempt to change the treatment plan may affect at least one otherparameter, which in turn may result in the development of a differenttreatment plan. Thus, the treatment planning system 160 can generate atailored radiation therapy treatment plan having these parameters inorder for the radiotherapy device 110 to provide radiotherapy treatmentto the patient.

In addition, the radiotherapy system 100 includes a display device 180and a user interface 190. The display device 180 may include one or moredisplay screens that display medical images, interface information,treatment planning parameters (e.g., contours, dosages, beam angles,etc.) treatment plans, a target, localizing a target and/or tracking atarget, or any related information to the user. The user interface 190may be a keyboard, a keypad, a touch screen or any type of device that auser may input information to radiotherapy system 100.

In order for the radiation therapy device 110 to localize and track atarget tumor in an anatomical region of interest, for example, processor112 can execute the target localization module 128. The targetacquisition module 120 may utilize the adaptive filter model 126generated by the training module 124, as described later in thisdisclosure. Further, the adaptive filter model can include particulardomain information (e.g., a spatial domain or a frequency domain),location information (e.g., 3D location in a volume, 3D offsetinformation, or 2D displacement information). The target localizationmodule 128 uses at least one adaptive filter model 126 generated by atraining module 124, as described below.

Furthermore, any and all components of the radiotherapy system 100, inan embodiment, may be implemented as a virtual machine (e.g., VMWare,Hyper-V, and the like). For instance, a virtual machine can be softwarethat functions as hardware. Therefore, a virtual machine can include atleast one or more virtual processors, one or more virtual memories, andone or more virtual communication interfaces that together function ashardware. For example, the OIS 150, the TPS 160, the image acquisitiondevice 170 could be implemented as a virtual machine. Given theprocessing power, memory, and computational capability available, theentire radiotherapy system could be implemented as a virtual machine.

FIG. 2 illustrates an example of one type of radiation therapy treatmentdevice 200, for example, a Leksell Gamma Knife, manufactured by Elekta,AB, Stockholm, Sweden. The Gamma Knife can be configured to utilize thetarget localization module 128 (shown in FIG. 1), which may remotelyaccess MRI images (e.g., from the image acquisition device 170) tolocalize a target tumor in the brain. In an embodiment, an MRIapparatus, as an image acquisition device 170, can be integrated withthe Gamma Knife. As shown in FIG. 2, during a radiotherapy treatmentsession, a patient 210 may wear a coordinate frame 220 to keep stablethe patient's body part (e.g., the head) undergoing surgery orradiotherapy. Coordinate frame 220 and a patient positioning system 230may establish a spatial coordinate system, which may be used whileimaging a patient or during radiation surgery. Radiotherapy device 200may include a protective housing 240 to enclose a plurality of radiationsources 250. Radiation sources 250 may generate a plurality of radiationbeams (e.g., beamlets) through beam channels 260. The plurality ofradiation beams may be configured to focus on an isocenter 270 fromdifferent directions. While each individual radiation beam may have arelatively low intensity, isocenter 270 may receive a relatively highlevel of radiation when multiple doses from different radiation beamsaccumulate at isocenter 270. In certain embodiments, isocenter 270 maycorrespond to a target under surgery or treatment, such as a tumor.

FIG. 3 illustrates another example of a type of radiation therapy device300 (e.g., a linear accelerator, referred to as a LINAC, manufactured byElekta, AB, Stockholm, Sweden). Using the linear accelerator 300, apatient 302 may be positioned on a patient table 304 to receive theradiation dose determined by a radiation therapy treatment plangenerated by the treatment planning system 160 (shown in FIG. 1). Theradiation treatment plan can be used to localize and track a 3D targetin a volume, such as a target organ or a target tumor located within theanatomy of the patient 302.

The linear accelerator 300 may include a radiation head 306 connected toa gantry 308 that rotates around the patient 302. The radiation head 306generates a radiation beam 310 that is directed toward the target organor target tumor. As the gantry 308 rotates, the radiation head 306 canrotate around the patient 302. While rotating, the radiation head 306may provide patient 302 with a plurality of varying dosages of radiationdepending upon the angle and the shape and size of the tumor accordingto the treatment plan generated by the treatment planning system 160(shown in FIG. 1). Because organs and tumors move within a patient'sbody, fast and accurate 3D localization of the target is important. Forinstance, a target organ or tumor may move because of various types ofmotion (e.g., respiratory, cardiac, peristalsis or other types ofpatient motion). Therefore, the linear accelerator 300 may be configuredto localize the target (e.g., organ or tumor) and track the targetduring radiation therapy treatment by using target localization module128.

In addition, below the patient table 304, a flat panel scintillatordetector 312 may be provided, which may rotate synchronously with theradiation head 306 around an isocenter 314 located on a target organ ora target tumor on the body of the patient 32. The flat panelscintillator can acquire images with the highest achievablesignal-to-noise ratio and can be used for verification of the amount ofradiation received by the patient 302 during any particular radiationtherapy treatment session (e.g., a radiation therapy treatment mayrequire multiple sessions of radiation therapy, where each session istypically referred to as a ‘fraction’). Further, such images are used todetermine the geometric accuracy of patient positioning relative to theradiation head 306.

The intersection of an axis 316 with the center of the beam 310,produced by the radiation head 306, is usually referred to as the“isocenter”. The patient table 304 may be motorized so the patient 302can be positioned with the tumor site at or close to the isocenter 314.For instance, the patient table 304 may change positions relative to oneor more other components of the linear accelerator 300, such as toelevate, change the longitudinal position, or the latitudinal positionof the patient 302 relative to a therapeutic radiation source located inthe radiation head 306.

In an embodiment the linear accelerator 300 may be integrated with theimage acquisition device 170 (shown in FIG. 1), such as a magneticresonance imaging device as a single apparatus (e.g., a MRI-Linac). Insuch a case, the MRI-Linac may include a “virtual couch” that can“virtually” adjust the alignment of the patient 302 relative to theradiation source when the patient table 304 is configured not to movebecause of the limited dimensions of a bore size through which thepatient table 304 is inserted during radiation therapy treatment. Insuch an embodiment the MRI-Linac can be used to determine a location ofthe target and track the target in the patient 302 using the targetlocalization module 128, such as to direct radiation therapy to apredetermined target.

FIG. 4 illustrates a process 400 for the training module 124 to generatean adaptive filter model 126. The adaptive filter model 126 (shown inFIG. 1) can be utilized by the target localization module 128 todetermine a location of a target and then track the target.

At 402, the training module 124 (shown in FIG. 1) receives a pluralityof images (e.g., 3D MRI, 4D MRI etc.) from the image acquisition device170 of a region of interest for a particular patient. The plurality ofimages, for example, can be 3D MRI images or 4D MRI images of a regionof interest that contain a target (e.g., a target organ, target tumor,etc.).

At 404, the training module 110 using processor 112 proceeds to extracta plurality of slices (e.g., 2D slices) that include the target (e.g.,target organ, target tumor, etc.) in the region of interest from theplurality if MRI images. The thickness, for example, of the 2D slicescan be predetermined (e.g., determined from information based on the 3DMRI volume or the 4D MRI volume) prior to beginning radiation therapytreatment. The information may include whether the 2D slices should bein a frequency domain or a spatial domain. Alternatively, the trainingmodule 110 may receive 2D slice information when the patient 402 isbeing treated with radiation therapy (e.g., in real-time). For example,an MRI-Linac could be used to treat the patient 402 with radiationtherapy. During this process of radiotherapy, the MRI-Linac can take aplurality of 3DMRI images or 4DMRI images during the treatment process.The extracted slices represent 2D slices of the anatomy of the patientthat surrounds the target. Further, the extracted 2D slices may beeither parallel or orthogonal to the motion of the target.

At 406, the processor 112 determines one or more 2D patches for eachextracted 2D slice. In an example, a 2D patch can correspond to an areaof the 2D slice image that can include the target. The 2D patch can beconfigured in any shape (e.g., a square, a rectangle, a circle, apolygon shape, etc.) and can vary in size (e.g., a 32×32 pixel patch, a64×64 pixel patch, etc.). For illustration, a square patch, for example,can be 32×32 pixels corresponding to the target in a 2D slice that is512×512 pixels. In an example, the 2D patch can include informationabout the target and information corresponding to an area that does notinclude the target (e.g., background information).

At 408, the processor 112 determines an offset value. The offset valueis, for example, a distance from a center of the 2D patch to a center ofthe target that is to be tracked. The 2D patches can include domaininformation (e.g., information about a spatial domain or frequencydomain of the 2D patches). Further, the offset information can includeinformation such as the displacement of the 2D patch from a referencepoint (e.g., a reference point corresponding to the target in thevolume), and information about a change in shape of the target (e.g.,deformation). In an example, the reference point can include acalculated center of the target (e.g., a tumor centroid). An offsetvalue is determined for each 2D patch containing the target. Therefore,each 2D patch has its own associated offset value. The plurality ofoffset values and their associated patches are stored in memory 116.

For instance suppose the center of the 2D patch has coordinates (e.g.,[xp, yp, zp]) and the center of the target tumor has coordinates (e.g.,[xt, yt, zt]) then the offset of the 2D patch center related to the 3Dlocation of the target tumor can be a vector (e.g., [ox,oy, oz]=[xt−xp,yt−yp, zt−zp]). Therefore, the processor 112 can track the target tumor.During tracking, the processor 112 can locate the center of the 2D patchfor a new patient setup (e.g., “on-line/real-time” as, for example[xpnew, ypnew, zpnew]). The processor 112 can then determine thelocation of the tumor (e.g., [xtnew, ytnew, ztnew]=[xpnew+ox, ypnew+oy,zpnew+oz]).

At 410, the processor 112 can use one or more types of adaptive filterdesign techniques to convert each of the patches into an adaptive filtermodel 126. For example, the following types of adaptive filter designsmay be used: matched filters, maximum margin correlation filters,synthetic discriminant function filters, least mean square filters, andthe like. Furthermore, the adaptive filter model 126 can include thedomain information and offset information as determined from the 2Dpatches corresponding to the target in the volume.

In general, the adaptive filter models 126, for example, “model” anobject (e.g., a tumor) to be located. A plurality of adaptive filtermodels 126 can be generated. For example, there can be an adaptivefilter model 126 for the top of the tumor, an adaptive filter model 126for the middle of the tumor, and an adaptive filter model 126 for thebottom of the tumor. In addition, adaptive filter models 126 can becreated for various parts of the human anatomy (e.g., one or more targetorgans of interest, such as prostate, breast, lung, heart, brain, etc.).In addition, adaptive filter model 126 can be generated for each targetof interest (e.g., one or more tumors in a particular organ—such asmultiple tumors in a lung; or in the case where cancer has metastasizedand there are one or more tumors in one or more organs, etc.).

Furthermore, the adaptive filter model 126 may be designed in either aspatial domain or in a frequency domain. In an example, the applicationof the adaptive filter model 126 to the 2D slices can be morecomputationally efficient in the frequency domain. The adaptive filtermodel 126 can be stored in memory 116 (shown in FIG. 1) and retrieved byprocessor 112 to be applied to subsequently acquired 2D slices (e.g., 2Dslices acquired in “real-time”/“on-line”) to predict the location of thetarget during radiation therapy treatment of the patient.

FIG. 5 illustrates an exemplary flowchart for a workflow process 500 forusing one or more adaptive filter models 126 to track a target during“real-time”/“on-line” radiation therapy treatment of the patient.

At 502, the process 500 begins by the radiotherapy device 10 (shown inFIG. 1) using processor 116 accesses the target localization module 128which retrieves a plurality adaptive filter models 126 designed for aregion of interest (e.g., prostate, breast, lungs, etc.) including thetarget tumor for a particular patient.

At 504, the processor 116 receives an acquisition protocol to provide tothe image acquisition device 170 to generate an initial set of 2Dslices. The protocol may include, for example, a location of slices tobe taken (e.g., lung, prostate, brain, kidney, etc.), an orientation ofthe slice (e.g., based on a predetermined potential organ motion—such asparallel or orthogonal to the slice to be taken), and a slice thickness(e.g., a 5 mm slice, a 10 mm slice, a 15 mm slice, etc.). A user mayprovide an initial estimate of the location of the target, which can beprovided through the user interface 190. The processor 116 can utilizethis initial estimate as an initial location of where to begin taking 2Dslices. Alternatively, such location information can be determinedautomatically by processor 116.

At 506, the radiotherapy device further receives a plurality of 2D MRIslices according to the protocol from the image acquisition device 170(e.g., a MRI device or a MRI-Linac device). The 2D images can correspondto the region of interest having the tumor. The 2D slices may beparallel or orthogonal to the tumor. The 2D slices may surround thetumor. Further, the 2D slices may be the region around the tumor.Typically, an MRI device provides 2D slices in the frequency domain.

At 508 through 514, described below, the processor 116 determines thelocation of a tumor on a 2D slice and its location in a 3D volume.

At 508, the processor 116 can convert the 2D slices to either thefrequency domain or the spatial domain. The 2D slices are converted tomatch the domain of the adaptive filter model 126. For example, if theadaptive filter model 126 was created in the spatial domain, the 2Dslices, for example, the processor 116 may convert the 2D slices to thespatial domain. Alternatively, if the adaptive filter model 126 wascreated in the frequency domain, the processor 116 may convert the 2Dslices to the frequency domain.

At, 510, the processor 116 can apply the adaptive filter model 126. Asdiscussed above the adaptive filter model 126 is a plurality of modelsgenerated from a plurality of 2D patches that have been trained by thetraining module 126. The processor 112 applies the plurality of adaptivefilter models 126 to the 2D slices. The 2D slice and the adaptive filtermodel, for example, can have the same orientation (e.g., orthogonal tothe direction of motion, parallel to the direction of motion, or both).In an example, the application of the adaptive filter model 126 to the2D slices can be more computationally efficient in the frequency domain.An example of how the adaptive filter model 126 can be applied to the 2Dslices e follows:

The adaptive filter model 126 can be denoted by a 2D template T(x,y) inthe spatial domain, and a 2D slice (e.g., a 2D image) can be denoted byI(x,y). Applying the adaptive filter model 126 to a particular locationof the 2D slice can provide a correlation-related “confidence score”.The confidence score, for example, provides an indication of how wellthe particular adaptive filter model matches a particular location ofthe 2D slice.

In an example, the better the match the adaptive filter model 126 hasrelative to a particular location of the 2D slice, the higher theconfidence score. The confidence score can be used to predict whetherand where the target tumor is located within the 2D slice. A number ofvarious types of correlation can be utilized to determine the confidencescore depending on the circumstances. For example, the following typesof correlation may be used: cross-correlation, normalizedcross-correlation, or correlation coefficient, which can be defined asfollows:

Cross-correlation: R(x,y)=Σ_(i,jεP)T(i,j)·I(x−i,y−j);

Normalized cross-correlation:

${{N\left( {x,y} \right)} = \frac{\sum\limits_{i,{j \in P}}{{T\left( {i,j} \right)} \cdot {I\left( {{x - i},{y - j}} \right)}}}{\sqrt{\sum\limits_{i,{j \in P}}{T\left( {i,j} \right)}^{2}}\sqrt{\sum\limits_{i,{j \in P}}{I\left( {{x - i},{y - j}} \right)}^{2}}}};$or

Correlation coefficient:

${C\left( {x,y} \right)} = \frac{\sum\limits_{i,{j \in P}}{\left( {{T\left( {i,j} \right)} - \overset{\_}{T}} \right) \cdot \left( {{I\left( {{x - i},{y - j}} \right)} - \overset{\_}{I}} \right)}}{\sqrt{\sum\limits_{i,{j \in P}}\left( {{T\left( {i,j} \right)} - \overset{\_}{T}} \right)^{2}}\sqrt{\sum\limits_{i,{j \in P}}\left( {{I\left( {{x - i},{y - j}} \right)} - \overset{\_}{I}} \right)^{2}}}$(e.g., the correlation coefficient is equivalent to the normalizedcross-correlation of the mean-corrected template and image).

In the above equations: P denotes the spatial domain of the template; Tand Ī denote the mean value of the template T and the slice I,respectively. Further, both N(x,y) and C(x,y) are bounded, for example,by −1≦N(x,y), C(x,y)<<+1, such that their values can be interpreted as aconfidence score relative to the perfect match result of +1. Theconfidence score can include information such as, for example, R, N, orC as defined above.

The three correlation computations can also be performed in thefrequency domain by applying the convolution theorem, for example: R=

⁻¹{

{T}·

{I}}, where

{ } and

⁻¹{ } denote the forward and inverse Fourier transform respectively.

At 512, the processor 112 can determine a response map, as a result ofthe application of an adaptive filter model 126 to the 2D slice. Aplurality of response maps can be created for a single 2D slice, foreach response map can correspond to a particular adaptive filter model.Both the adaptive filter model and the 2D slice can have the sameorientation. The response map may be generated in a spatial domain or afrequency domain. Both the 2D slice and the adaptive filter model 126have to be in the spatial domain to generate a response map in thespatial domain. Similarly, both the 2D slice and the adaptive filtermodel 126 have to be in the frequency domain to generate a response mapin the frequency domain. A response map can be created for every 2Dslice. The generated response map can be an image that indicates thedegree a particular adaptive filter model 126 matches various locationswithin a given 2D slice.

For example, an adaptive filter model A (not shown) can be applied bythe processor 112 to every location on a 2D slice. For each location,the processor 112 can determine a confidence score that indicates howwell, for instance, the adaptive filter A matches a particular locationof the 2D slice. The better the match is between the adaptive filtermodel A relative to the location on the 2D slice, the higher theconfidence score (e.g., closer to +1) for that particular location.Brighter areas on the response map image can indicate a better match,and therefore, a higher confidence score (e.g., between the adaptivefilter model A at the particular location of the 2D slice) than darkerareas. Brighter areas can have higher confidence scores than darkerareas in the response map. After, the adaptive filter model A is appliedto the 2D slice, a response map A can be generated. The processor 112can retrieve another adaptive filter model B (not shown), for example,and repeat the process of applying the adaptive filter model B to everylocation on the 2D slice to generate a response map B. Thus, theprocessor using the target localization module can apply a plurality ofadaptive filter models to the 2D slice to generate a plurality ofresponse maps, which can be stored in memory 116. At each location ofthe 2D slice, a confidence score is generated corresponding to theadaptive filter model utilized. A high confidence score may correspondto a location on the 2D slice where the target tumor may be located.

At 514, the processor 112 can predict the target tumor location on the2D slice based on the confidence score. For example, for a particularlocation on the response map, there can be multiple confidence scorevalues, where each confidence score can correspond to a particularadaptive filter model. In order to predict the tumor's location, theprocessor 112 can select the maximum confidence score value for aparticular location on the 2D slice. Alternatively, the processor 112can use a weighted average of all the confidence scores for a particularlocation on the 2D slice. In an example, the processor 112 may computethe weighted average of the top N confidence scores, where N is apredetermined number. Using the confidence scores generated by usingmultiple adaptive filter models can increase the accuracy of theprediction. Therefore, a high confidence score for a particular locationcan indicate the location of the target on the 2D slice, and a lowconfidence score can indicate the tumor is not at that location. Aconfidence score closest to +1 can indicate that a part of the tumor orthe whole tumor is located on the 2D slice.

If processor 112 determines that the 2D slice does not contain a tumor,the process 500 follows 501 to return to 506 to acquire another 2Dslice. The process 500 may cause the processor 112 to adjust theacquisition protocol in order to acquire the next 2D slice.Alternatively, the tumor may be included in one or more 2D slicesdepending on slice thickness, the size of the tumor, the orientation ofthe tumor and other factors. For example, if the bottom of the tumor isincluded in the present 2D slice, the processor 112 may use offsetinformation to determine what 2D slice should subsequently be acquired(e.g., a 2D slice containing the centroid of the tumor or may be a 2Dslice including the top of the tumor).

For example, using information from a particular adaptive filter modeland its corresponding response map, the processor 112 can determine thelocation of the tumor on previous 2D slice. Further, using offsetinformation associated with the adaptive filter model, the processor 112can also estimate a location (e.g., the center) of the tumor in 3D. Forexample, the processor 112 may determine that the tumor intersects the2D slice at, for example, the (5,10)-th pixel location and the center ofthe tumor can be 5 mm from the center of the 2D slice in the slicenormal direction. In order to estimate the next position of the tumor(e.g., track the tumor as it moves) or to acquire more completeinformation about the shape of the tumor (e.g., whether the shape of thetumor has changed) the processor 112 can adjust various parameters(e.g., a location where a subsequent 2D slice will be acquired,acquiring a subsequent 2D slice that is parallel to the previous slicebut in a different position, acquiring a 2D slice that is orthogonal tothe previous slice, adjusting the thickness of the subsequent slice,etc.). Further, using offset information from the previous adaptivefilter model, the processor 112 can also estimate the next position ofthe tumor. Thus, the processor 112 can track the movement of the tumorin real-time as a patient undergoes radiation therapy.

At 516, the processor 112 can track the tumor as the patient undergoesin “real-time” radiation therapy treatment. In addition, the processor112 can estimate the shape of the tumor when multiple 2D slices areutilized.

FIG. 6 pictorially illustrates an exemplary adaptive filter modelapplied to a 2D MRI slice to determine a location of a target (e.g., aliver). At 602, an example of an adaptive filter model in the spatialdomain is pictorially represented. The processor 112 (shown in FIG. 1)can use the target localization module 129 to apply the adaptive filtermodel shown in 602 to a particular 2D slice, which is also in thespatial domain. By applying the exemplary adaptive filter model 602 tothe 2D slice, the processor 112 can determine for each location of the2D slice how well the exemplary adaptive filter model 602 matches aparticular location of the 2D slice. Advantageously, the processor 112can compute each 2D slice in approximately 300 ms. The targetlocalization module 129 generates an exemplary response map, which ispictorially illustrated in 604. The exemplary response map 604 canrepresent how well the adaptive filter model 602 matched each locationof the 2D slice. Brighter areas shown in the exemplary response map 604indicate a better match, and therefore, a higher confidence score,between the adaptive filter model 602 at the particular location of the2D slice than darker areas. Brighter areas can have higher confidencescores than darker areas in the response map 604. As illustrated, theresponse map 604 indicates a bright area 608 that can correspond to thehighest confidence score for this 2D slice, as determined by processor112. The bright area 608 on the response map 604, therefore, cancorrespond to a location of a target on this particular 2D slice.Therefore, the processor 112 has determined the location of the target608 that is located on this particular 2D slice. Further, because thelocation of the patient's anatomy from where the 2D slice was generatedby the image acquisition device 170, the processor 112 can determine thelocation of the target 608, on the patient's anatomy as shown in 606.The red area in 606 corresponds, for example, to the adaptive filtermodel 602 overlayed on the patient's anatomy. In another embodiment, forexample, the target can represent a tumor.

Alternatively, a response map, for example, can be represented in thefrequency domain depending on if the 2D slices and the adaptive filtermodel are in the frequency domain. To generate a response map in thefrequency domain, processor 112 can perform a fast-fourier transform(FFT) on the 2D slice. The processor 112 applies the adaptive filtermodel, by performing a point-by-point multiplication in the frequencydomain. To generate the response map, the processor 112 can perform aninverse-fast-fourier transform.

Additional Notes

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

The exemplary disclosed embodiments describe systems and methods fortracking a target (e.g., a tumor) in the anatomy of a patient, while thepatient undergoes radiation therapy treatment. The foregoing descriptionhas been presented for purposes of illustration. It is not exhaustiveand is not limited to the precise forms or embodiments disclosed.Modifications and adaptations of the embodiments will be apparent fromconsideration of the specification and practice of the disclosedembodiments.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, an apparatus,system, device, article, composition, formulation, or process thatincludes elements in addition to those listed after such a term in aclaim are still deemed to fall within the scope of that claim. Moreover,in the following claims, the terms “first,” “second,” and “third,” etc.are used merely as labels, and are not intended to impose numericalrequirements on their objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include softwarecode, such as microcode, assembly language code, a higher-level languagecode, or the like. The various programs or program modules can becreated using a variety of software programming techniques. For example,program sections or program modules can be designed in or by means ofJava, Python, C, C++, assembly language, or any known programminglanguages. One or more of such software sections or modules can beintegrated into a computer system and/or computer-readable media. Suchsoftware code can include computer readable instructions for performingvarious methods. The software code may form portions of computer programproducts or computer program modules. Further, in an example, thesoftware code can be tangibly stored on one or more volatile,non-transitory, or non-volatile tangible computer-readable media, suchas during execution or at other times. Examples of these tangiblecomputer-readable media can include, but are not limited to, hard disks,removable magnetic disks, removable optical disks (e.g., compact disksand digital video disks), magnetic cassettes, memory cards or sticks,random access memories (RAMs), read only memories (ROMs), and the like.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods can be modified in anymanner, including by reordering steps or inserting or deleting steps. Itis intended, therefore, that the specification and examples beconsidered as example only, with a true scope and spirit being indicatedby the following claims and their full scope of equivalents.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments can be combined with each other in various combinations orpermutations. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed:
 1. A method, implemented by a processor, for locating atarget in a three-dimensional (3D) volume, the method comprising:receiving a 3D medical image containing a region of interest of thetarget; extracting, by the processor, a plurality of two-dimensional(2D) slices from the 3D medical image; determining, by the processor, a2D patch for each 2D slice, wherein the 2D patch corresponds to an areaof the 2D slice associated with the target; determining, for at leastone 2D patch, an offset value reflecting a distance between a locationof the at least one 2D patch and a location of the target; andconverting, by the processor, each 2D patch to an adaptive filter modelfor determining a location of the region of interest, wherein at leastone adaptive filter model includes a function for determining aconfidence score when the corresponding adaptive filter model is appliedto a 2D image acquired by an image acquisition device during a treatmentsession, the confidence score indicating a relative degree of matchbetween the at least one adaptive filter model and a particular locationof the 2D image.
 2. The method of claim 1, wherein: converting the 2Dpatch includes using an adaptive filter technique, the adaptive filtertechnique including at least one of matched filters, maximum margincorrelation filters, synthetic discriminant functions filters, or leastmean squares filters.
 3. The method of claim 1, wherein the offset valuecomprises a distance vector between a point on the at least one 2D patchand a reference point within the target in the 3D volume.
 4. The methodof claim 1, wherein at least one of the plurality of 2D slices issubstantially parallel to a motion of the target in the 3D volume. 5.The method of claim 1, wherein at least one of the plurality of 2Dslices is substantially orthogonal to a motion of the target in the 3Dvolume.
 6. The method of claim 1, wherein each adaptive filter model hasan identical orientation to an orientation of the corresponding 2D slicethat includes the 2D patch from which the adaptive filter model isconverted.
 7. The method of claim 1, wherein each 2D slice is either ina frequency domain or in a spatial domain.
 8. The method of claim 1,wherein each adaptive filter model is either in a frequency domain or ina spatial domain.
 9. The method of claim 1, wherein each adaptive filtermodel and the corresponding 2D slice that includes the 2D patch fromwhich the adaptive filter model is converted are in a same domain,wherein the domain is either a frequency domain or a spatial domain. 10.The method of claim 1, further comprising converting, by the processor,at least one of the plurality of 2D slices to a domain of a firstadaptive filter model, wherein the domain is at least one of a frequencydomain or a spatial domain, and wherein the first adaptive filter modelis in a similar orientation to the at least one of the plurality of 2Dslices.
 11. The method of claim 1, further comprising adjusting, by theprocessor, a parameter prior to extracting the plurality of 2D slicesfrom the 3D medical image.
 12. The method of claim 11, wherein theparameter comprises a target location parameter.
 13. The method of claim11, wherein the parameter comprises an initial target locationparameter.
 14. The method of claim 11, wherein the parameter comprises aslice orientation parameter.
 15. The method of claim 14, wherein theslice orientation parameter comprises at least one of a sagittalorientation, a coronal orientation, or an axial orientation.
 16. Themethod of claim 11, wherein the parameter comprises a slice thicknessparameter.
 17. The method of claim 11, wherein the parameter comprises atarget motion direction parameter indicative of a direction of a motionof the target in the 3D volume.
 18. The method of claim 1, wherein theprocessor is configured to generate the adaptive filter modelscorresponding to the plurality of 2D slices.
 19. The method of claim 18,wherein each adaptive filter model corresponds to a particular region ofinterest that includes at least a portion of the target.
 20. The methodof claim 18, wherein the processor is configured to combine two or moreadaptive filter models, each adaptive filter model corresponding to adifferent slice orientation.
 21. The method of claim 1, wherein at leastone of the 2D patches corresponds to an area of the corresponding 2Dslice that includes at least a portion of the target.
 22. The method ofclaim 1, wherein at least one of the 2D patches includes informationcorresponding to an area surrounding the target but does not include thetarget.
 23. The method of claim 1, wherein each 2D patch is either in aspatial domain or in a frequency domain.
 24. The method of claim 1,wherein each 2D patch is associated with a corresponding offset valuereflecting a distance between a location of the 2D patch and a locationof the target.
 25. A system for tracking a target in a three-dimensional(3D) volume using two-dimensional (2D) image slices of the 3D volume,the system comprising: a processor; and a memory operatively coupled tothe processor and storing computer-executable instructions that whenexecuted by the processor, causes the processor to perform a method, themethod comprising: receiving a 3D medical image containing a region ofinterest of the target; extracting a plurality of 2D slices from the 3Dmedical image; determining a 2D patch for each 2D slice, wherein the 2Dpatch corresponds to an area of the 2D slice associated with the target;determining, for at least one 2D patch, an offset value reflecting adistance between a location of the at least one 2D patch and a locationof the target; and converting each 2D patch to an adaptive filter modelfor determining a location of the region of interest, wherein at leastone adaptive filter model includes function for determining a aconfidence score when the corresponding adaptive filter model is appliedto a 2D image acquired by an image acquisition device during a treatmentsession, the confidence score indicating a relative degree of matchbetween the at least one adaptive filter model and a particular locationof the 2D image.
 26. The method of claim 1, further comprising:determining parameters for acquiring a future 2D image during thetreatment session based on the offset value associated with the at leastone 2D patch.